Most machine learning (ML) algorithms are highly configurable.
Their hyperparameters must be chosen carefully, as their choice often impacts the model performance.
Even for experts, it can be challenging to find well-performing hyperparameter configurations.
Automated machine learning (AutoML) systems and methods for automated HPO have been shown to yield considerable efficiency compared to manual tuning by human experts [1]}.
However, these approaches mainly return a well-performing configuration
and leave users without insights into decisions of the optimization process.
Questions about the importance of hyperparameters or their effects on the resulting performance often remain unanswered.
Not all data scientists trust the outcome of an AutoML system due to the lack of transparency [2]}.
Consequently, they might not deploy an AutoML model, despite all performance gains. Providing insights into the search process may help increase trust and facilitate interactive and exploratory processes: A data scientist could monitor the AutoML process and make changes to it (e.g., restricting or expanding the search space) already during optimization to anticipate unintended results.
